EFFECT OF DOMESTIC DEBT ON ECONOMIC GROWTH IN THE EAST AFRICAN COMMUNITY

American Journal of Research Communication www.usa-journals.com

Babu, et al., 2015: Vol 3(9) 73

EFFECT OF DOMESTIC DEBT ON ECONOMIC GROWTH IN THE EAST AFRICAN COMMUNITY

James Ochieng Babu1*, Symon Kiprop1, Aquilars M. Kalio1, Mose Gisore2

1Department of Economics, Egerton University, Kenya, P.O. Box 536-20115, Egerton, Kenya 2Department of Economics and Public Policy, Technical University of Kenya, Kenya

* Tel. +254720036153, Email: [email protected]

Abstract

This paper empirically explores the effect of domestic debt, as a share of Gross Domestic

Product (GDP), on economic growth in the East Africa Community (EAC) over the period 1990-

2010. This study was based on the Solow growth model augmented for debt. Levin-Lin-Chu test

(LLC) was used to investigate the properties of the data with respect to Unit roots. The Hausman

specification test was used to select the panel fixed-effects model, which was corrected for

heteroscedasticity. The results show that domestic debt has a positive significant effect on per

capita GDP growth rate in the EAC. The policy implication is to promote sustainable levels of

domestic borrowing to enhance growth.

Keywords: Domestic Debt; Economic Growth; East African Community

{Citation: James Ochieng Babu, Symon Kiprop, Aquilars M. Kalio, Mose Gisore. Effect of domestic debt on economic growth in the east African community. American Journal of Research Communication, 2015, 3(9): 73-95} www.usa-journals.com, ISSN: 2325-4076.

1. Introduction

Domestic Public Debt is mainly debt owed to holders of Government securities such as Treasury

Bills and Treasury Bonds. Governments usually borrow by issuing securities, government bonds

and bills. Governments borrow for two reasons namely: when the projected revenue targets fall

http://www.usa-journals.com/


Babu, et al., 2015: Vol 3(9) 74

short of the projected expenditure and to pay off maturing loans (Ponzi games) which is typical

with domestic debt.

“Reasonable” levels of borrowing by a developing country are likely to enhance its economic

growth, both through capital accumulation and productivity growth. Countries at early stages of

development have small stocks of capital and are likely to have investment opportunities with

rates of return higher than in advanced economies. As long as they use the borrowed funds for

productive investment and they do not suffer from macroeconomic instability, policies that

distort economic incentives, or sizable adverse shocks, growth should increase and allow for

timely debt repayments (Pattillo et al. 2004).

Appropriate use of debt could lead to improved socio-economic growth and thus, better

standards of living. In order to make debt effective, there is need for far reaching reforms in the

management of the public sector. However in most cases, resources from debt have not been

used as effectively, for example, projects financed by international loans have, due to lack of

adequate or realistic planning, failed to generate sufficient resources to service the debt

borrowed. Therefore socio-economic development is compromised since the government spends

huge sums on loan repayments, hence reducing money it spends on education, health and other

social amenities, which mainly target the poor, who comprise the majority of the population

(KENDREN, 2009).

The literature on the origins of the African debt crisis lists a number of factors as its cause. The

oil price shocks of 1973-74 and 1978-79, the expansion of the Eurodollar, a rise in public

expenditure by African governments following increases in commodity prices during the early

1970s, recession in the industrialized nations and subsequent fall in commodity prices, as well as


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rises in real world interest rate are all mentioned as major factors. Surprisingly, almost all of this

literature focuses on the post-independence period, with a greater part of the analysis contained

therein relating to the 1970s and 80s (Alemayehu, 2002).

The East African Community (EAC) is among the fastest growing regions. Growth rates have

picked up strongly in the EAC countries over the last two decades hence outpacing the rest of

Sub-Saharan African (SSA) since 2000. During 2005–2010, per capita income growth reached

3.7 percent a year in the EAC, compared to 3.2 percent for SSA as a whole, and almost

quadruple the rate achieved in the previous 15-year period. Part of the recent high growth is

“catching up” after years of very poor growth. In the last part of the 20th century, the region

suffered periods of severe civil strife and bouts of economic instability. Since then, the region

has been committed to strong policies.

However, growth within the EAC has been uneven. Rwanda, Tanzania, and Uganda have had the

longest periods of high growth. Uganda’s growth acceleration started earlier than in the other

countries and has lasted more than 20 years, with per capita income growth averaging 3.4

percent a year during 1990–2010. Growth in Rwanda and Tanzania has been strong since the

early 2000s. After a period of stagnation, growth is picking up in Kenya, the largest of the five

economies, averaging 1.9 percent a year since 2005 compared to minus 0.2 percent in 1990–

2004, providing momentum for the region as a whole. Output declined in Burundi in most of the

period since 1990 (reflecting periods of political conflict) but has shown signs of recovery in

recent years (McAuliffe et al. 2012).

Once a country borrows, it has to pay the loan amount plus interest and associated cost (Debt

servicing). This will therefore imply that the government uses resources which could be used to


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meet its expenditures to pay the debt. Debt service has crowded-out funding for social and

capital expenditures in these countries. After debt servicing and salaries, there is little left for

core functions of the government, that is, education, health, basic infrastructure, and other

essential services to create an enabling environment for the private sector. For example, in 1989,

Kenya’s debt service was more than a third of its export earnings. On the other hand Tanzania’s

debt payment was 86 percent of export earnings in 1987 (Kiringai, 2002).

Since the EAC member countries are striving for sustainable economic growth, they need to

manage their fiscal deficit hence public debt. In order to bring it down, these countries must

conquer the challenges of increasing revenues, expanding avenues for new investments and

curtailing unessential public expenditures that can drive these economies to a higher growth path

while limiting the current account deficit to sustainable levels.

2. Literature on Domestic Debt and Growth

2.1 Theoretical Literature

Domestic debt may have positive as well as negative impacts on economic growth. In the

traditional view, a tax cut financed by government borrowing would have many effects on the

economy. The immediate impact of the tax cut would be to motivate consumer spending. Higher

consumer spending affects the economy in both short run and long run. In the short run, higher

consumer spending would raise the demand for goods and services and thus raise output and

employment. As the marginal propensity to consume is higher than marginal propensity to save,

the increase in private savings falls short of government dis-saving. This increases the real

interest rate in the economy hence encouraging capital inflow from abroad.


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In the long run, the higher interest rate would discourage investment and thus crowd out private

investment. The lower domestic savings mean a smaller capital stock. The inflow from abroad

would result in greater foreign debt. The higher aggregate demand results in a higher price level

that adjusts over time and the economy returns to a natural rate of output. The lower investment

eventually leads to a lower steady state capital stock and a lower level of output. Therefore, the

overall impact when considering the long-run period would be smaller total output and

eventually lower consumption and reduced economic welfare. This is also referred to as the

burden of public debt, as each generation burdens the next, by leaving behind a smaller

aggregate stock of capital (Sheikh et al. 2010).

According to WB and IMF (2001), extensive use of domestic borrowing can have severe

repercussions on the economy. Domestic debt service can consume a significant part of

government revenues, especially given that domestic interest rates are higher than foreign ones.

The interest cost of domestic borrowing can rise quickly along with increases in the outstanding

stock of debt, especially in shallow financial markets. The increase in interest rates may be even

more pronounced if the investor base is relatively narrow, since the government may be held

hostage by a particular group of investors.

Domestic debt financing leads to crowding-out of private investment. When issuing domestic

debt, governments tap domestic private savings that would otherwise be available to private

sector. This is normally followed by an increase in domestic interest rates, if these are flexible,

adversely affecting private investment. However, even when interest rates are controlled,

domestic borrowing can lead to credit rationing and crowding-out of private sector investment

(Fischer and Easterly, 1990).


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2.2 Empirical Literature

Putunoi and Mutuku (2013) studies the impact of domestic debt on economic growth of Kenya

over the period 2000-2010 using the Engel-Granger residual based and Johannes VAR based

cointegration tests and revealed that domestic debt markets play an increasingly important role in

supporting economic growth. They find that domestic debt expansion has a positive long-run and

significant effect on economic growth.

Sheikh et al. (2010) investigates the impact of domestic debt on economic growth of Pakistan for

the period 1972-2009 by applying ordinary least squares (OLS) technique. The study finds that

domestic debt favourably affects economic growth in Pakistan implying that the funds generated

through domestic borrowing have been used partially to finance those expenditures of

government that contribute to growth of GDP. The principle is that domestic as well as external

debt should be spent for long-term development purposes. Another reason for the positive

relationship between domestic debt and economic growth in Pakistan may be that domestic debt

is marketable.

Maana et al. (2008) explores the impact of domestic debt on Kenya’s economy covering the

period 1996 to 2007 using a modified Barro growth regression model. The study established that

domestic debt expansion had a positive but not significant effect on economic growth during the

period. However, the study found no evidence that the growth in domestic debt crowds-out

private sector lending in Kenya.

Abbas and Christensen (2007) analysed optimal domestic debt levels in low-income countries

and emerging markets between the period 1975-2004 using Granger Causality Regression model

and found that moderate levels of marketable domestic debt as a percentage of GDP have


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significant positive effects on economic growth. The study also provided evidence that debt

levels exceeding 35 percent of total bank deposits have negative impact on economic growth.

Adoufu and Abula (2010) examine the effect of domestic debt on the Nigerian economy during

the period 1986-2005 using OLS technique. The findings reveal that domestic debt has

negatively affected the growth of the economy and recommends that the government should

introduce efforts to resolve the outstanding domestic debt.

2.3 The Augmented Solow Model

According to Brauninger (2003), following Mankiw, Romer and Weil in 1992, it is assumed that

households fix the saving and the educational spending ratio. So we have an augmented Solow

model. An increase in public debt is used to redistribute every individual’s tax burden from the

youth to the middle age increases the steady growth rate.

We assume a Cobb-Douglas technology with CRS 𝑌 = 𝐴𝐾𝛼 𝐿𝛽 . Let H be human capital and N

be the number of workers. Then 𝐻/𝑁 is human capital per worker, 𝐿 = (𝐻/𝑁)𝑁. Therefore, a

production function is obtained as:

𝑌 = 𝐴𝐾𝛼𝐿𝛽

Output Y is used for consumption, investment, government purchases and spending on

education, 𝑌 = 𝐶 + 𝐼 + 𝐺 + 𝑍.

Considering the public debt dynamics, the government raises loans and levies an income tax in

order to finance government purchases and interest payments on public debt. The government

spends a fixed share of national income on goods and services 𝐺 = 𝑔𝑌 with the purchase ratio g

constant. In addition, the government borrows a specified portion of national income 𝐵 = 𝑏𝑌


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with the deficit ratio b constant. The budget deficit in turn adds to public debt �̇� = 𝐵. The

government pays the interest rate r on public debt D, so the public interest amounts to rD. The

government imposes a tax at the flat rate t on both factor income and debt income

𝑇 = 𝑡(𝑌 + 𝑟𝐷). Thus, the government budget constraint can be written as 𝐵 + 𝑇 = 𝐺 + 𝑟𝐷.

Next regard the dynamics of physical and human capital accumulation. Disposable income is the

sum of factor income and debt income, net of taxes respectively, 𝑌𝑌 = 𝑌 + 𝑟𝐷 − 𝑇. Human

capital can be augmented by spending on education, �̇� = 𝑍. By backward substitution, one

obtains �̇� = 𝑠(𝑌 + 𝑟𝐷 − 𝑇) − 𝐵 and �̇� = 𝑧(𝑌 + 𝑟𝐷 − 𝑇), with 𝐵 + 𝑇 = 𝐺 + 𝑟𝐷, 𝐵 = 𝑏𝑌 and

𝐺 = 𝑔𝑌, which results to:

�̇� = (1 + 𝑏 − 𝑔)𝑠𝑌 − 𝑏𝑌 and �̇� = (1 + 𝑏 − 𝑔)𝑧𝑌.

The model can be presented by a system of six equations.

𝑌 = 𝐴𝐾𝛼𝐻𝛽 (2.1)

𝛾 = 𝛼𝛼𝐾

(2.2)

�̇� = (1 + 𝑏 − 𝑔)𝑠𝑌 − 𝑏𝑌 (2.3)

�̇� = (1 + 𝑏 − 𝑔)𝑧𝑌 (2.4)

�̇� = 𝑏𝑌 (2.5)

𝑏𝑌 + 𝑡(𝑌 + 𝑟𝐷) = 𝑔𝑌 + 𝑟𝐷 (2.6)

Here, α, β, b, g, s, z, D and K are exogenous, where r, t, �̇�, 𝐻,̇ �̇� and Y are endogenous.

In the steady state, physical and human capital grow at the same rate as output,


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𝐾� = 𝐻� = 𝑌 � (2.7)

We obtain the steady state growth rate as

𝑌� = 𝐻� = 𝑌 � = [𝑧(1 + 𝑏 − 𝑔)]𝛽[𝑠(1 + 𝑏 − 𝑔) − 𝑏]𝛼𝐴 (2.8)

3. Methodology

The basic regression equation that was used to estimate the relationship between domestic debt

and economic growth is therefore of the type:

𝑙𝑙𝑙𝐺𝐷𝑙𝑖,𝑡 = 𝛽𝑙𝑙𝑋𝑖,𝑡 + 𝛾 𝑙𝑙𝐷𝐷𝑖,𝑡 + 𝜇𝑖 + 𝑣𝑡 + 𝜀𝑖,𝑡 (3.1)

Where;

𝑙𝐺𝐷𝑙𝑖,𝑡- is the growth rate of real GDP per capita. It is the dependent variable.

Xi,t -consists of different explanatory variables that were used. The variables are

government size, openness, private investment and terms of trade. These variables are

known to be consistently associated with growth.

DD- is the internal debt variable, that is, the ratio of domestic debt to GDP.

μi – unobserved country-specific effects. It captures the effect of each cross-section

(country) which does not vary over time. This is because the study is only interested in

analyzing the impact of variables that vary across time.

vt –unobserved time-specific effects.

𝜀𝑖,𝑡– is the error term.


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4. Data

The data employed in the study consists of a panel of five countries covering the period 1990-

2010. The dependent variable is real GDP per capita growth rate (economic growth), for the debt

variable, the indicator which was used here is the total domestic debt-to-GDP ratio (DD). Other

than the debt variable, different explanatory variables were used to control for other factors that

influence economic growth, the variables are private investment (pinv), government expenditure

(gvte), terms of trade (tot) and openness (opns). The source of data for variables: economic

growth, private investment and terms of trade was mainly World Development Indicators (WDI)

2011 of the World Bank. However the study obtained domestic debt data from the Annual Debt

Reports of the Central Banks of the five EAC countries, and openness data from the Penn World

Tables version 7.1.

Recent studies such as Bosworth and Collins (2003) suggest that it is better to focus on a core set

of explanatory variables that have been shown to be consistently associated with growth and

evaluate the importance of other variables conditional on inclusion of the core set. It is therefore

upon such basis that the variables in this study were selected and justified. These variables have

also been shown to be consistently associated with growth in the findings of Sala-i-Martin et al.

(2004).

5. Empirical Analysis and Presentation of Results

5.1 Panel Unit Root Tests

One of the econometric problems in empirical analysis is non-stationarity of time series data.

Spurious regression and inconsistent results are likely to be obtained if we run a regression in the

level form while the variables in the model are non-stationary and therefore inferences based on


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such data are likely to be meaningless. Due to this econometric problem, the variables in the

model were tested for panel unit roots using the Levin-Lin-Chu (LLC) method. The Levin- Lin-

Chu test is based on the following hypotheses:

H0: Each time series contains a unit root.

H1: Each time series is stationary.

The results of the panel unit root tests for the variables are summarized and presented in Table 1.

Table 1: LLC Tests for Stationarity/Unit root tests for all variables (variables in levels)

Variable LLC (Level) LLC

(First Difference)

LLC (P-Value)

Level

Order of Integration

LNRGDP -3.2612

-2.4582

-

0.0011 I(0)

LNGVTE -1.8619

-1.7187

-4.1003

-2.9438

0.1783 I(1)

LNPINV -1.5662

-1.3780

-4.8588

-3.1609

0.5261 I(1)

LNOPNS -1.4523

-1.3151

-5.1897

-3.2571

0.5978 I(1)

LNTOT -1.1113

-1.0207

-4.2445

-2.9929

0.8646 I(1)

LNDD -4.4059

-1.7314

-5.3321

-3.3191

0.0417 I(1)

Critical Values: -2.460 (1%); -2.180 (5%); -2.040 (10%).

From the results in Table 1, only the variable real GDP per capita growth rate (RGDP) was found

to be stationary at 5 percent level of significance and therefore integrated of order zero (I (0)),

while the rest of the variables, GVTE, PINV, OPNS, TOT and DD are integrated of order one (I

(1)), that is, they were found to be stationary after differencing them once.


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5.2 Cointegration Tests

The panel data property of each variable was established and obtaining their order of integration,

the next step was to establish whether the non-stationary variables are cointegrated. Usually,

when variables are differenced to attain stationarity, the long-run properties are lost.

Cointegration means that there is a long-run relationship between two or more non-stationary

variables. Since the dependent variable (RGDP) was stationary (I (0)), it was not possible to

check for cointegration in that particular case.

5.3 Hausman Test

Hausman test was carried out to decide whether to use fixed or random effects model. The

results are presented in Table 2.

Table 2: Hausman Test Results

Hausman fixed random -Coefficients-

(b) fixed

(B) random

(b-B) Difference

S. E.

LNGVTE -0.3834427 -0.2266823 -0.1567603 0.0796634

LNPINV 0.2593462 -0.0819139 0.3412601 0.6912984

LNOPNS -0.3222148 -0.2419819 -0.080233 0.0278517

LNTOT -0.0467704 -0.0129315 -0.0338389 0.0156052

LNDD 0.2095102 0.1512991 0.0582112 0.0317513

Test: H0: difference in coefficients not systematic

𝝌𝟐 (5) = 15.69 Prob> 𝝌𝟐 = 0.0470


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From the Hausman test results in Table 2, the p-value is 0.0470, less than 0.05. This shows that

the value is significant and therefore fixed effects model is applicable in regression. The fixed

effects model was therefore chosen based on Hausman test carried out.

5.4: Test for Cross-Sectional Dependence

Cross-sectional dependence is the interaction between cross-sectional units. Cross-sectional

dependence leads to efficiency loss for least squares and invalidates conventional t-tests and F-

tests which use standard variance-covariance estimators. The study employed the Breush-Pagan

Lagrange Multiplier (LM) test of independence. The null hypothesis is that the residuals across

entities are not correlated.

Table 3: Cross-Sectional Dependence Test Results

Correlation matrix of residuals

__e1 __e2 __e3 __e4 __e5

__e1 1.0000

__e2 -0.2576 1.0000

__e3 -0.2646 0.0048 1.0000

__e4 0.2014 0.0582 -0.0512 1.0000

__e5 -0.1886 0.2654 0.4216 0.2525 1.0000

Breusch-Pagan LM test of independence: 𝝌𝟐 (10) = 10.610, Pr = 0.3887

𝑒𝑖 − residuals from the cross-sectional units, 𝑖 = 1, 2, … , 5.

Based on 20 complete observations over panel units

The p-value is greater than 0.05, therefore insignificant. It tells us that there is no cross-sectional

dependence.


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5.5 Heteroscedasticity Test

Heteroscedasticity is a situation where the error terms do not have constant variance. It can be

caused by measurement errors and if there are subpopulation differences or other interaction

effects. Heteroscedasticity does not lead to biased parameter estimates, however, the standard

errors are biased if heteroscedasticity is present. This in turn leads to bias in test statistics and

confidence intervals.

The test results for heteroscedasticity are presented below.

Modified Wald test for GroupWise Heteroscedasticity in Fixed Effect Regression Model 𝐻0: 𝜎𝑖2 = 𝜎2 ∀ 𝑖

Table 4: Heteroscedasticity Test Results

𝝌𝟐 (5) 219.85 Prob> 𝝌𝟐 0.0000

The p-value is less than 0.05, it is significant which reveals the presence of heteroscedasticity.

Therefore the null hypothesis for homoscedasticity is rejected. Regression was then done by

correcting for heteroscedasticity using the option ‘robust’ in fixed effects. The use of robust

standard errors does not change coefficient estimates, but (because the standard errors are

changed) the test statistics gives a reasonable accurate p-values. Hence when heteroscedasticity

is present, robust standard errors tend to be more appropriate.

5.6 Test for Serial Correlation

Serial correlation occurs when the error terms from different time periods (or cross-section

observations) are correlated. According to Drukker (2003), serial correlation in linear panel-data

models biases the standard errors and causes the results to be less efficient, therefore, serial

correlation should be identified in the idiosyncratic error term in a panel data model. A new test


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by Wooldridge (2002) is very attractive because it requires relatively few assumptions and is

easy to implement.

A test for serial correlation was conducted and the results presented below.

Wooldridge Test for Autocorrelation in Panel Data

H0: no first order autocorrelation

F (1, 4) = 4.182

Prob > F = 0.1103

From the results, the p-value is greater than 0.05, therefore, we fail to reject the null hypothesis

and conclude that the data does not have serial correlation.

5.7 Domestic Debt and Economic Growth in the EAC

In this section, the hypothesis regarding the growth- domestic debt nexus was tested. The

economic growth-domestic debt analysis is based on panel data; this is because it gives the

chance to control for endogeneity, omitted variables and also explores the data across time. The

basic estimation technique applied here is panel fixed-effects corrected for heteroscedasticity.

Table 5: Results of Economic Growth and Domestic Debt Regression

Dependent Variable: LNRGDP Method: Fixed Effects Regression

Variable Coefficient Std. Error t-Statistic p-value

CONSTANT 1.5042 0.3134 4.80 0.003

DLNGVTE -0.1201 0.0759 -1.58 0.117

DLNPINV 0.3394 0.0568 5.98 0.001

DLNOPNS 0.0250 0.0526 0.48 0.636

DLNTOT -0.4229 0.0500 -8.45 0.000

DLNDD 0.1166 0.0489 2.38 0.019

Adj. R2 = 0.5907 Durbin Watson = 1.9837

F (9, 115) = 3.4528 p-value (F) = 0.000843


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The results show that expansion in private investment positively impacts growth. The coefficient

of private investment has its hypothesized sign (positive) and statistically significant at 1 percent

level, meaning that a 10 percent increase in private investments leads to economic growth by

3.394 percent. Investment in an economy leads to increased capital spending as it involves

construction of industries, buying new machines, investing in skills and education increases

labour productivity. This has led to increase in the productive capacity of the EAC countries

which boost economic growth. These results conform to the findings by Jorgenson (2003),

Hoover and Perez (2004) and Abdi (2004) who found a strong link between investment in

general and machinery and equipment investment in particular with economic growth.

From the results, a 10 percent change in terms of trade leads to a 4.229 percent decline in

economic growth in the EAC. These results were as expected (negative relationship) since the

region faces adverse terms of trade caused by the nature of the commodities they specialize in.

Terms of trade volatility tends to induce volatility in consumer spending, investment, inflation

and economic growth thereby making macroeconomic policies difficult to implement. The EAC

countries are developing and usually face sharp swings in export prices which contribute to

increased volatility in growth of GDP. Studies by Mendoza (1997) and Broda (2003) have also

concluded that changes in terms of trade can account for half of the output volatility in

developing countries; furthermore the EAC member countries’ exports are small and

undiversified specifically the case of Rwanda and Burundi leading to weak growth performance.

The EAC member countries like other developing countries are more sensitive to terms of trade

volatility than their industrial counter parts that specialize in production of manufactured

products. This is the reason why terms of trade are negatively related to economic growth in

EAC.


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From the results shown in the Table 5, the debt coefficient is positive and statistically significant

at 5 percent level of significance which indicates that if domestic debt levels rise by 10 percent,

the economy grows by 1.166 percent, holding the other independent variables in the model

constant. The positive impact of domestic debt on economic growth is due to the fact that

domestic debt levels in EAC countries are still moderate and sustainable and therefore promotes

growth. These results are consistent with previous studies by Maana et al. (2008) and Abbas and

Christensen (2007) who argue that moderate levels of domestic debt could have a positive effect

on the economy, more so if the debt is marketable. Debt that is securitized, bears positive real

interest rates and is diversely held is found to be robustly friendlier to growth. In contrast,

Fischer and Easterly (1990) and WB and IMF (2001) argue that domestic borrowing can lead to

crowding out of private sector investment and hence a decline in economic growth.

Moderate levels of domestic debt promote financial deepening and institutional and foreign

participation which then drives economic growth. The outlook on domestic issuance capacity in

low middle income countries looks broadly favourable. The quality and span of domestic debt

markets can have a significant impact on the optimal size of domestic debt. A higher level of

domestic debt can likely be sustained without compromising growth if the domestic debt is

issued in the form of marketable securities, bears positive real interest rates, and is issued to

investors outside the banking system. Putunoi and Mutuku (2013) also established that domestic

debt expansion had a positive significant effect on Kenya’s economic growth. The relationship

between economic growth and domestic debt is evidenced by the fact that domestic debt markets

promote financial depth and economic efficiency. This is as a result of increased expansion of

capital market and financial sector liberalization in the EAC recently which drives growth. For


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example in Kenya, the formation of Capital Markets Authority has increased the activities in the

Nairobi Stock Exchange.

The regression results in Table 5 show that the coefficient of government expenditure is

negative. The effect of government expenditure on economic growth of EAC countries was as

hypothesised (positive or negative), but in this case it is negative though not statistically

significant at any conventional level of significance. This could be as a result of corruption,

redirection of funds, large recurrent expenditure and misappropriation of government funds in

these countries.

The impact of openness on economic growth is positive but not statistically significant at any

conventional level of significance. This could be as a result of the EAC countries specializing in

primary product exports and have small trade shares in the international market. Therefore, the

beneficial effects from openness could not be felt properly in the economies.

6. Conclusion and Policy Implications

The main focus of this study was to establish the effect of domestic debt on the economic growth

of the EAC member countries. Regression results of domestic debt and economic growth

revealed that domestic debt expansion has a positive effect on economic growth of the EAC

member countries. However, the study established that terms of trade volatility faced by the EAC

countries negatively affects their economic growth.

The governments of the EAC member countries should promote moderate levels of domestic

borrowing which can be sustained by the respective countries as it promotes economic growth if

used in productive and efficient avenues. However, domestic debt is usually expensive and


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should be minimized since it has wider negative macroeconomic effects for instance, if interest

rate on treasury bills rise, banks target treasury bills and not lending to borrowers, interest rates

and inflation also goes up.

In order to promote faster growth, terms of trade can be improved through processing of the

EAC exports. This could be supplemented through increased export earnings by export

promotion strategy. Improvement in terms of trade increases domestic real income which raises

the level of public investments and therefore growth. The governments should also ensure food

stability to avoid importation of food which is a common practice in the region.

The governments can privatize some of the public assets in order to cut large public expenditure

and also raise revenue on a temporary basis. Non-growth recurrent expenditure should not

exceed the accepted levels, for example, the wage bill for Kenya has surpassed. Corruption and

leakages in public funds should be reduced so as to ensure that government expenditure

promotes growth.

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A. Appendix

A 1 Variables, Measurement and Sources of Data

RGDP- Real GDP per Capita Growth. This paper uses Real GDP per capita, which is the

annual percentage growth rate of GDP per capita based on constant local currency. Islam (1995)

uses per capita values. Data Source: WDI (2011) Data Base.

PINV–Private Investments. Investment refers to the purchase of goods that are not consumed

today but are used in the future to create wealth. Theoretically, Investment is the key to

economic growth, if investment rises in an economy, aggregate demand also rise and therefore

economic growth. Jorgenson (2003) obtained that investment in tangible assets is the most

important source of economic growth in the Group of Seven (G7) nations. The contribution of

capital input exceeds that of productivity for all countries for all periods. This variable is

measured as a ratio of GDP. Data source: WDI (2011) Data Base.

GVTE - Government Expenditure. Government expenditure refers to general government final

consumption expenditure as a share of GDP. Larger government provide public goods, further

increases in government expenditure can increase the disposable incomes of the citizens which

encourages growth. However, large government spending can lead to transfer of additional

resources away from the most productive sectors of the economy to government, where they are

used less efficiently and thus undermining economic growth. Cooray (2009) concluded that

expansion of government expenditure contributes positively to economic growth. However a

study by Barro (1991) suggested that large government expenditure has negative impact on

economic growth. Data Source: WDI (2011) Data Base.


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TOT - Terms of Trade. Terms of trade refers to the price of a country’s exports (PX) relative to

the price of its imports (PM). Where PX is a price index for all export goods due to the fact that

countries export more than one good, PM is a price index for all import goods. Mendoza (1997)

proposes a stochastic growth model whereby terms of trade uncertainty can adversely affect

savings and growth. Data Source: WDI (2011) Data Base.

OPNS - Openness. Openness refers to the sum of exports and imports of goods and services as a

share of GDP. According to World Bank (1993), significant growth rates are often associated

with countries embracing the ongoing globalization and increasing openness to the international

exchange of goods and services as well as ideas and technologies. Participation in the

international economy was the primary source of growth in many East Asian countries that have

experienced fast economic development during the past 50 years. This variable is measured as

the ratio of imports (M) plus exports (X) to GDP [(M +X)/ GDP]. Data Source: Penn World

Tables (7.1).

DD-Domestic Debt. Domestic debt refers to money owed to lenders within a country, that is,

holders of government securities such as treasury bills and bonds. It is expressed as a ratio of

GDP. According to WB and IMF (2001), extensive use of domestic borrowing can have severe

repercussions on the economy through crowding out of private investments. However, Abbas

and Christensen (2007) argue that moderate levels of domestic debt could have a positive effect

on the economy if the debt is marketable. Data Source: Kenya National Bureau of Statistics and

the Central Banks of EAC member countries.

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EFFECT OF DOMESTIC DEBT ON ECONOMIC GROWTH IN THE EAST AFRICAN COMMUNITY